Notes about Cutting-Edge Technologies and Everything

With the challenge of the vast amount of data generated by devices at the edge of networks, new architecture needs a well-established data service model that accounts for privacy concerns. This paper presents an architecture of data transmission and a data portfolio with privacy for fog-to-cloud (DPPforF2C). We would like to propose a practical data model with privacy from a digitalized information perspective at fog nodes. In addition, we also propose an architecture for implicating the privacy of DPPforF2C used in fog computing. Technically, we design a data portfolio based on the Message Queuing Telemetry Transport (MQTT) and the Advanced Message Queuing Protocol (AMQP). We aim to propose sample data models with privacy architecture because there are some differences in the data obtained from 10T devices and sensors. Thus, we propose an architecture with the privacy of DPPforF2C for publishing data from edge devices to fog and to cloud servers that could be applied to fog architectu...

With the upcoming HDP 3.1 release, we also bring about some exciting innovations to enhance our Kafka offering – New Hive Kafka Storage Handler (for SQL Analytics) – View Kafka topics as tables and execute SQL via Hive with full SQL Support for joins, windowing, aggregations, etc. New Druid Kafka Indexing Service (for OLAP Analytics) – View Kafka topics as cubes and perform OLAP style analytics on streaming events in Kafka using Druid. HDF 3.3 includes the following major innovations and enhancements: Core HDF Enhancements Support for Kafka 2.0, the latest Kafka release in the Apache community, with lots of enhancements into security, reliability and performance. Support for Kafka 2.0 NiFi processors NiFi Connection load balancing – This feature allows for bottleneck connections in the NiFi workflow to spread the queued-up flow files across the NiFi cluster and increase the processing speed and therefore lessen the effect of the bottleneck. MQTT performance improvements inc...

To gather insights on the current and future state of open source software (OSS), we talked to 31 executives. This is nearly double the number we speak to for a research guide and believe this reiterates the popularity of, acceptance of, and demand for OSS. We began by asking, "What Open Source software do you use?" As you would expect, most respondents are using several versions of open source software. Here's what they told us: Apache Apache Cassandra, Elassandra  (ElasticSearch + Cassandra) , Spark, and Kafka  (as the core tech we provide through our managed service) are the big ones for us. We find that the governance arrangements and independence of the Apache Foundation make a great foundation for strong open source projects. 95% of what we do with big data is open source. We use  Apache Hadoop  and contribute back to grow skills and expertise. We use so much that it would be impossible to list. The core of our software is based on  Apache So...